View source for IEEE TETC SI 2017 : IEEE Transactions on Emerging Topics in Computing, Special Issue on Scholarly Big Data

{{Event 
|Acronym=IEEE TETC SI 2017 
|Title=IEEE TETC SI  2017 : IEEE Transactions on Emerging Topics in Computing, Special Issue on Scholarly Big Data 
|Type=Conference 
|Field=computer science
|Start date=Invalid date
|End date=2017/08/21T09:54:04 
|City=N/A 
|Country=undefined
|Homepage=https://www.computer.org/cms/Computer.org/transactions/cfps/cfp_tetcsi_sbd.pdf
|Submission deadline=2017/12/01T12:00:00
}}
CALL FOR PAPERS
IEEE Transactions on Emerging Topics in Computing Special Issue on Scholarly Big
Data

IEEE Transaction on Emerging Topics in Computing (TETC) seeks original
manuscripts for a Special Issue/Section on Scholarly Big Data scheduled to
appear in the fourth issue of 2018.

Recent years have witnessed the rapid growth of scholarly information due to
advancements in information and communication technologies. Scholarly big data
is the vast quantity of research output, which can be acquired from digital
libraries, such as journal articles, conference proceedings, theses, books,
patents, experimental data, etc. It also encompasses various scholarly related
data, such as author demography, academic social networks, and academic
activity. The abundance of scholarly data sources enables researchers to study
the academic society from a big data perspective. The dynamic and diverse nature
of scholarly big data requires different data management techniques and advanced
data analysis methods. Today’s researchers realize that new scholarly-big-data
specific platform/management/techniques/ are needed. Therefore, a set of
emerging topics such as scholarly big data acquisition, storage, management and
processing are important issues for the research community. Manuscripts
submitted to TETC should be computing focused.

This special issue focuses on covering the most recent research results in
scholarly big data management and computing. The issue welcomes both theoretical
and applied research (e.g. platforms and applications). It will encourage the
effort to share data, advocate gold-standard evaluation among shared data, and
promote the exploration of new directions. Topics of interest include (but not
limited to):
* New approaches to search and crawling of scholarly big data from various data
sources
* Methods for storing, indexing, and query processing for scholarly big data
* Practices for scholarly big data management and sharing
* Heterogeneous scholarly big data source integration, especially for novel
datasets (e.g. online social media)
* Scholarly big data analysis, mining, and visualization
* Design of next generation scholarly big data platforms and systems
* Algorithms for measuring the scientific impact of articles, authors,
institutions, etc.
* Scientific information network analysis
* Recommendation tools and techniques
* Scientific community detection and clustering
* Graph and text mining in scholarly big data
* Privacy and security issues
* Services and applications

Submitted articles must not have been previously published or currently
submitted for journal publication elsewhere. As an author, you are responsible
for understanding and adhering to the IEEE submission guidelines. You can access
them at the IEEE Computer Society web site, www.computer.org. These should be
carefully read before manuscript submission. Please submit your manuscript to
Manuscript Central at https://mc.manuscriptcentral.com/tetc-cs

Please note the following important dates.
Submission Deadline: Dec. 1, 2017
Reviews Completed: Mar. 1, 2018
Major Revisions Due (if Needed): April 1, 2018
Reviews of Revisions Completed (if Needed): May 1, 2018
Minor Revisions Due (if Needed): June 1, 2018
Notification of Final Acceptance: August 1, 2018
Publication Materials for Final Manuscripts Due: Sept 1, 2018
Publication date: Last Issue of 2018 (December Issue)

Guest Editors

Feng Xia
Dalian University of Technology, China
f.xia@ieee.org

Huan Liu
Arizona State University, USA
huanliu@asu.edu

C. Lee Giles
Pennsylvania State University, USA
giles@ist.psu.edu


Kuansan Wang
Microsoft Research, USA
Kuansan.Wang@microsoft.com